Methods and systems for dynamically selecting alternative content based on real-time events during device sessions using cross-channel, time-bound deep reinforcement machine learning
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Solution Overview
Problem
Conventional methods for generating personalized content during device sessions fail to dynamically update content based on real-time user interactions and data, leading to reduced user engagement and wasted screen real estate due to the inability of existing machine learning models to process multiple events and goals efficiently.
Innovation Solution
A cross-channel, time-bound deep reinforcement machine learning system that uses a Deep Deterministic Policy Gradient (DDPG) architecture to dynamically select alternative content by combining the outputs of multiple machine learning models with different goals, optimizing predictions through continuous learning and incorporating user feedback and real-time events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional machine learning models are used to select content quickly, then response speed is improved, but accuracy in selecting content based on multiple goals is worsened
Solution Approach 1:
The system segments the content selection task into multiple independent machine learning models, each trained to achieve a specific goal (e.g., user engagement, revenue generation, information delivery). Each model processes different aspects of the selection problem separately, then their outputs are combined to form the final content recommendation. This allows each model to be optimized for its specific function while maintaining overall system accuracy.
Solution Approach 2:
The system merges the outputs of multiple machine learning models with different goals into a unified content selection decision. By combining the predictions from several specialized models rather than relying on a single general-purpose model, the system achieves both speed (each model is simple and fast) and accuracy (collective wisdom of multiple models).
2Measurement precision
If multiple machine learning models with different goals are used to optimize content selection, then content selection accuracy is improved, but system complexity is worsened
Solution Approach 1:
The system employs a universal framework that can accommodate multiple machine learning models with different goals through a standardized interface and output combination mechanism. This multi-functional architecture allows the system to handle various content selection objectives (engagement, revenue, information) simultaneously while maintaining a consistent system structure, thereby managing complexity through standardization.
3Ease of manufacture
If conventional static content interfaces are used, then implementation simplicity is improved, but user engagement is worsened
Solution Approach 1:
The system transitions from static content interfaces to dynamic content selection that adapts in real-time based on user behavior, contextual information, and multiple optimization goals. The content displayed changes dynamically according to user interactions and system predictions, significantly improving engagement while maintaining implementation feasibility through the use of pre-trained machine learning models.
Data Source
AI summary
Methods and systems are described herein for dynamically selecting alternative content based on real-time events during device sessions using a cross-channel, time-bound deep reinforcement machine learning. The use of this architecture allows for alternative content to be selected in a time-bound and continuous manner that provides predictions in a dynamic environment (e.g., an environment in which user data is continuously changing and new events are continuously occurring) and with an increased success rate (e.g., new data and events are factored into each prediction). For example, in the system each round of predictions considers both input features, which can change by a user's actions, state of a user interface, and/or previous responses and states.